The role of point‐of‐care platelet function testing in predicting postoperative bleeding following cardiac surgery: a systematic review and meta‐analysis
Bibliographic record
Abstract
This systematic review and meta-analysis appraises the utility of point-of-care platelet function tests for predicting blood loss and transfusion requirements in cardiac surgical patients, and analyses whether their use within a transfusion management algorithm is associated with improved patient outcomes. We included 30 observational studies incorporating 3044 patients in the qualitative assessment, and nine randomised controlled trials including 1057 patients in the meta-analysis. Platelet function tests demonstrated significant variability in their ability to predict blood loss and transfusion requirements. Their use within a blood transfusion algorithm demonstrated a reduction in blood loss at longest follow-up (mean difference -102.9 ml (95% CI -149.9 to -56.1 ml), p < 0.001), and transfusion of packed red cells (RR 0.86 (95% CI 0.78-0.94), p = 0.001) and fresh frozen plasma (RR 0.42 (95% CI 0.30-0.59), p < 0.001). Viscoelastic methods used in combination with other platelet function tests achieved greater reduction in blood loss (mean difference -111.8 ml (95% CI -174.9 to -49.1 ml), p = 0.0005) compared with their use alone (mean difference -90.6 ml (95% CI 166.1-15.0 ml), p = 0.02). We conclude that incorporation of point-of-care platelet function tests into transfusion management algorithms is associated with a reduction in blood loss and transfusion requirements in cardiac surgery patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".